Dataviz in R

Scripting, post-processing & interactive web

Brown bag lunch, June 18, 2015

Markus Kainu
ESS, Team F

Fork me on GitHub

Creative Commons Licence

Content

Usage
Print Web
Static Bitmap One Two
Vector Three Four
Interactive Javascript graphs Five Six
Web applications Seven Eight
  • Basics of graphics - bitmap vs. vector
  • Why to use R for graphics - scripting vs. point & click
  • Static graphics in R
  • Post-processing R graphs
  • The WEB: interactive graphics and web applications

Vector graphics vs. bitmap graphics

bitmap vector
file extensions .jpg, .png, .gif .eps, .pdf, .svg, .ai
common example digital photo google maps
consists of million of pixels points, lines and polygons
file size large small
software Gimp (Photoshop) Inkscape (Illustrator)
good for web, printing (in high-res) print, post-processing
exclusively used digital photography maps and graphs

You can make vector graphics into bitmap graphics, but not the other way around

Case 1: Static graphics for print & web

Population data from FAOSTAT

library(FAOSTAT)
library(knitr)
library(dplyr)
library(ggplot2)
dat <- getFAOtoSYB(domainCode = "OA", elementCode = 551,itemCode = 3010) # Download rural population
dat1 <- dat$entity
dat <- getFAOtoSYB(domainCode = "OA", elementCode = 561,itemCode = 3010) # Download urban population
dat2 <- dat$entity
dat <- merge(dat1,dat2,by=c("FAOST_CODE","Year"))

dim(dat)

[1] 19854     4

kable(head(dat)) # print 5 first rows
FAOST_CODE Year OA_3010_551 OA_3010_561
100 1961 375928 82699
100 1962 382709 85253
100 1963 389708 87908
100 1964 396938 90670
100 1965 404412 93541
100 1966 412128 96528

Population data from FAOSTAT

names(dat) <- c("FAOST_CODE","Year","rural_pop","urban_pop")
dat$total_pop <- dat$rural_pop + dat$urban_pop # Compute total population
dat$rural_share <- round(dat$rural_pop / dat$total_pop * 100,1) # Compute rural population share of total population
library(countrycode) # Add the country name & continent using countrycode-package
dat$FAOST_CODE[dat$FAOST_CODE == 41] <- 351
dat$cntry <- countrycode(dat$FAOST_CODE, origin = "fao", destination = "country.name")
dat$cont <- countrycode(dat$FAOST_CODE, origin = "fao", destination = "continent")
dat <- na.omit(dat)

dim(dat)

[1] 16475     8

kable(head(dat)) # print 5 first rows
FAOST_CODE Year rural_pop urban_pop total_pop rural_share cntry cont
100 1961 375928 82699 458627 82.0 India Asia
100 1962 382709 85253 467962 81.8 India Asia
100 1963 389708 87908 477616 81.6 India Asia
100 1964 396938 90670 487608 81.4 India Asia
100 1965 404412 93541 497953 81.2 India Asia
100 1966 412128 96528 508656 81.0 India Asia 
library(ggplot2)
library(dplyr)
p <- ggplot(data=dat, 
            aes(x=rural_pop,y=urban_pop))
p + geom_point()

plot of chunk unnamed-chunk-5 

pie_data <- dat %>% filter(Year == 2015) %>% 
  group_by(cont) %>% summarise(share = sum(rural_pop))
pie_data <- pie_data %>% mutate(sum = sum(share)) 
p <- ggplot(pie_data, aes(x=sum/2, y = share, fill =cont, width = sum))
p <- p + geom_bar(position="fill", stat="identity") 
p + coord_polar("y")

plot of chunk unnamed-chunk-6 

plot_data <- dat[dat$Year == 2015,]
p <- ggplot(data=plot_data, 
            aes(x=cntry,y=rural_share))
p + geom_bar(stat="identity")

plot of chunk unnamed-chunk-7 

p <- ggplot(data=dat, 
            aes(x=Year,y=rural_share,group=cntry))
p + geom_point() + geom_line()

plot of chunk unnamed-chunk-8

Share of rural population

plot_data <- dat %>% filter(Year == 2015)
p <- ggplot(data=plot_data, 
            aes(x=cntry,y=rural_share))
p + geom_bar(stat="identity")

plot of chunk unnamed-chunk-9

Share of rural population

plot_data$cntry <- factor(plot_data$cntry, levels=arrange(plot_data, -rural_share)$cntry)
p <- ggplot(data=plot_data, 
            aes(x=cntry,y=rural_share))
p + geom_bar(stat="identity")

plot of chunk unnamed-chunk-10

Share of rural population

plot_data <- arrange(plot_data, -rural_share)[1:20,] # top 20
p <- ggplot(data=plot_data, aes(x=cntry,y=rural_share,fill=cont))
p + geom_bar(stat="identity")

plot of chunk unnamed-chunk-11

Share of rural population

p <- ggplot(data=plot_data, aes(x=cntry,y=rural_share,fill=cont))
p <- p + geom_bar(stat="identity")
p + coord_flip()

plot of chunk unnamed-chunk-12 

p <- ggplot(data=plot_data, aes(x=cntry,y=rural_share,fill=cont))
p <- p + geom_bar(stat="identity")
p <- p + coord_flip()
p <- p + scale_fill_manual(values=c("#1b9e77","#d95f02","#7570b3","#e7298a","#66a61e"))
p <- p + theme_minimal()
p <- p + theme(text = element_text(family = "Open Sans"),
               title = element_text(size = 20),
               axis.title = element_text(size=14),
               legend.position = "top")
p <- p + labs(title="Share of rural population", x=NULL,y="Share of rural population (%)")
p <- p + guides(fill = guide_legend(title = "Continent", title.position = "left", title.hjust=.5))
p

plot of chunk unnamed-chunk-13

Static Choropleth maps

library(gisfao)
library(scales)
library(tidyr)
library(stringr)
shape <- fao_world
FAOST_CODE <- as.character(shape$FAOST_CODE)
df.d <- data.frame(FAOST_CODE, VarX = rep(NA, nrow(shape)))
dat$FAOST_CODE[dat$FAOST_CODE == 351] <- 41
mapdata <- merge(dat %>% filter(Year %in% c(1965,1980,2000,2015)), df.d, by.x = "FAOST_CODE", all.y = TRUE)
mapdata <- mapdata[c("FAOST_CODE","Year","rural_share")]
mapdata$Year <- paste0("X",mapdata$Year)
mapdata <- spread(mapdata, Year, rural_share)
mapdata$XNA <- NULL
row.names(mapdata) <- mapdata$FAOST_CODE
row.names(shape) <- as.character(shape$FAO_CODE)
mapdata <- mapdata[order(row.names(mapdata)), ]
shape <- shape[order(row.names(shape)), ]
shape2 <- maptools::spCbind(shape, mapdata)
shape2$id <- rownames(shape2@data)
map.points <- fortify(shape2, region = "id")
map.df <- merge(map.points, shape2, by = "id")

map.df <- gather(map.df, "Year", "rural_share", 28:31)
map.df$Year <- str_replace_all(map.df$Year, "X", "Year ")
map <- ggplot(data=map.df, aes(long,lat,group=group))
map <- map  + geom_polygon(aes(fill = rural_share),colour=alpha("white", 3/4),size=.2)
map <- map + theme(legend.position = "top", 
                          legend.background=element_rect(colour=NA, fill=NA),
                          axis.text = element_blank(), axis.title = element_blank(), axis.ticks = element_blank())
map <- map + facet_wrap(~Year, ncol = 2)

Static Choropleth maps

map

plot of chunk unnamed-chunk-15

Animated choropleth maps

library(gisfao)
library(scales)
library(tidyr)
library(stringr)
library(RColorBrewer)
library(gridExtra)
shape <- fao_world
FAOST_CODE <- as.character(shape$FAOST_CODE)
df.d <- data.frame(FAOST_CODE, VarX = rep(NA, nrow(shape)))
dat$FAOST_CODE[dat$FAOST_CODE == 351] <- 41
mapdata <- merge(dat, df.d, by.x = "FAOST_CODE", all.y = TRUE)
mapdata <- mapdata[c("FAOST_CODE","Year","rural_share")]
mapdata$Year <- paste0("Year ",mapdata$Year)
mapdata <- spread(mapdata, Year, rural_share)
mapdata$XNA <- NULL
row.names(mapdata) <- mapdata$FAOST_CODE
row.names(shape) <- as.character(shape$FAO_CODE)
mapdata <- mapdata[order(row.names(mapdata)), ]
shape <- shape[order(row.names(shape)), ]
shape2 <- maptools::spCbind(shape, mapdata)
shape2$id <- rownames(shape2@data)
map.points <- fortify(shape2, region = "id")
map.df <- merge(map.points, shape2, by = "id")
map.df <- gather(map.df, "Year", "rural_share", 28:118)
map.df$Year <- as.character(map.df$Year)
map.df$Year <- str_replace(map.df$Year, "Year.", "")
map.df$Year <- factor(map.df$Year)
map.df$Year <- as.numeric(levels(map.df$Year))[map.df$Year]
myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))
y_dat <- data.frame()



for(y in unique(map.df$Year)[!is.na(unique(map.df$Year))]) {

  map.df_subset <- map.df[map.df$Year == y,]
  map <- ggplot(data=map.df_subset, aes(long,lat,group=group))
  map <- map  + geom_polygon(aes(fill = rural_share),colour=alpha("white", 3/4),size=.2)
  map <- map + theme(legend.position = "top", 
                          legend.background=element_rect(colour=NA, fill=NA),
                          axis.text = element_blank(), axis.title = element_blank(), axis.ticks = element_blank())
  map <- map + labs(title=paste("Year",y))
  map <- map + scale_fill_gradientn(limits = c(0,100),colours= myPalette(100))
  # plot
  line_data <- map.df_subset[!duplicated(map.df_subset[c("FAO_CODE")]),]
  line_data <- line_data[c("FAO_CODE","rural_share","ADM0_NAME","Year")]
  y_dat <- rbind(y_dat,line_data)
  plot <- ggplot(y_dat, aes(x=Year,y=rural_share,group=FAO_CODE,color=rural_share)) +
                   geom_line() +
                   coord_cartesian(xlim=c(1960,2050)) +
    scale_color_gradientn(limits = c(0,100),colours= myPalette(100)) +
    coord_cartesian(ylim=c(0,100))


  png(file=paste0("map/Year.",y,".png"), width=1000, height=700, res=120)
  grid.arrange(arrangeGrob(map,plot, widths=c(3/5,2/5), ncol=2))
  dev.off()
}
system("convert -delay 40 -loop 0 map/*.png map/maps.gif")

Exporting for post-processing

# barplot
ggsave(p, file="output/barplot.svg", width=14, height=8)
ggsave(p, file="output/barplot.pdf", width=14, height=8)
ggsave(p, file="output/barplot.png")

ggsave(map, file="output/map.svg", width=14, height=8)
ggsave(map, file="output/map.pdf", width=14, height=8)
ggsave(map, file="output/map.png")

-> post-processing in Inkscape & Gimp

Case 2: interactive graphics & applications for the web

Interactive graphics

interactive graphics interactive web applications
typical example javascript plot Trade data explorer
look and feel amazing but limited amazing and unlimited
depends online libraries, shipped with data Needs a full R instance
R htmlwidgets shiny
good for showing off & showing off & explorative wiz

Interactive maps

library(gisfao)
library(dplyr)
library(sp)
library(leaflet)


shape <- fao_world
FAOST_CODE <- as.character(shape$FAOST_CODE)
VarX <- rep(NA, nrow(shape))
df.d <- data.frame(FAOST_CODE, VarX)
dat2 <- merge(dat, df.d, by.x = "FAOST_CODE", all.y = TRUE)
row.names(dat2) <- dat2$FAOST_CODE
row.names(shape) <- as.character(shape$FAO_CODE)
dat2 <- dat2[order(row.names(dat2)), ]
shape <- shape[order(row.names(shape)), ]
shape2 <- maptools::spCbind(shape, dat2)

# Define the region and color variables and title:
main <- "Rural population"
color = "rural_share"

# Define color palette
palette <- leaflet::colorNumeric(c("#fef0d9","#fdcc8a","#fc8d59","#e34a33","#b30000"), 
                                 domain = shape2$rural_share)

# Define the text for popup box
state_popup <- paste0("<strong>Country: </strong>", shape2$ADM0_NAME, 
                      "<br><strong>", main, ": </strong>", 
                      round(shape2@data[,c(color)], digits=2))

# Generate interactive visualization
leaflet(data = shape2) %>% 
  addTiles() %>% 
  setView(lng = 12.30, lat = 41.54, zoom = 3) %>% 
  addPolygons(fillColor = ~palette(get(color)), 
              fillOpacity = 0.7, 
              color = "#000000", 
              weight = 1,
              popup = state_popup) %>% 
  addLegend("bottomright", pal = palette, values = ~rural_share,
    title = "Share of rural population",
    labFormat = labelFormat(prefix = "%")
  )

Interactive maps

Interactive time-series

R for dataviz

Pros

  • R has good graphics
  • Scripting makes your ideas/graphs re-usable
  • organising your code is important
  • growing expertise in ESS/FAO

Cons

  • scripting is slow the first time